Multiple-Clothing Detection and Fashion Landmark Estimation Using a Single-Stage Detector

Kim, Hyo Jin; Lee, Doo Hee; Niaz, Asim; Kim, Chan Yong; Memon, Asif Aziz; Choi, Kwang Nam

doi:10.1109/access.2021.3051424

Cited by 20 publications

(10 citation statements)

References 33 publications

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“…At the same time, BiFPN also shows the best efficiency in multi-scale feature fusion. At present, the deep learning model based on EfficientDet and BiFPN is being applied to a variety of research fields, such as forest fire prevention (Xu et al, 2021), estimation of fashion landmarks (Kim et al, 2021), detection of garbage scattering areas (You et al, 2020), etc.…”

Section: Introductionmentioning

confidence: 99%

Occlusion Robust Wheat Ear Counting Algorithm Based on Deep Learning

2021

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Counting the number of wheat ears in images under natural light is an important way to evaluate the crop yield, thus, it is of great significance to modern intelligent agriculture. However, the distribution of wheat ears is dense, so the occlusion and overlap problem appears in almost every wheat image. It is difficult for traditional image processing methods to solve occlusion problem due to the deficiency of high-level semantic features, while existing deep learning based counting methods did not solve the occlusion efficiently. This article proposes an improved EfficientDet-D0 object detection model for wheat ear counting, and focuses on solving occlusion. First, the transfer learning method is employed in the pre-training of the model backbone network to extract the high-level semantic features of wheat ears. Secondly, an image augmentation method Random-Cutout is proposed, in which some rectangles are selected and erased according to the number and size of the wheat ears in the images to simulate occlusion in real wheat images. Finally, convolutional block attention module (CBAM) is adopted into the EfficientDet-D0 model after the backbone, which makes the model refine the features, pay more attention to the wheat ears and suppress other useless background information. Extensive experiments are done by feeding the features to detection layer, showing that the counting accuracy of the improved EfficientDet-D0 model reaches 94%, which is about 2% higher than the original model, and false detection rate is 5.8%, which is the lowest among comparative methods.

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Section: Introductionmentioning

confidence: 99%

Occlusion Robust Wheat Ear Counting Algorithm Based on Deep Learning

2021

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“…In the field of fashion image analysis, deep learning models with high computational requirements have been a challenge. To address this, the proposed study [20] Summary of the related work surveyed, detailed in Table 1.…”

Section: 5-another Fashionnetmentioning

confidence: 99%

Underwater Object Detection Based on Improved EfficientDet

Jia

et al. 2022

Remote Sensing

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Intelligent detection of marine organism plays an important part in the marine economy, and it is significant to detect marine organisms quickly and accurately in a complex marine environment for the intelligence of marine equipment. The existing object detection models do not work well underwater. This paper improves the structure of EfficientDet detector and proposes the EfficientDet-Revised (EDR), which is a new marine organism object detection model. Specifically, the MBConvBlock is reconstructed by adding the Channel Shuffle module to enable the exchange of information between the channels of the feature layer. The fully connected layer of the attention module is removed and convolution is used to cut down the amount of network parameters. The Enhanced Feature Extraction module is constructed for multi-scale feature fusion to enhance the feature extraction ability of the network to different objects. The results of experiments demonstrate that the mean average precision (mAP) of the proposed method reaches 91.67% and 92.81% on the URPC dataset and the Kaggle dataset, respectively, which is better than other object detection models. At the same time, the processing speed reaches 37.5 frame per second (FPS) on the URPC dataset, which can meet the real-time requirements. It can provide a useful reference for underwater robots to perform tasks such as intelligent grasping.

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“…Chen et al [82] also adopted this method for mode landmark recognition: They proposed a Clothes Landmark Detection network based on Feature Pyramid Network and designed the Dual Attention Feature Enhancement (DAFE) module to improve the feature representations while recovering the size of the feature maps. Li et al [83] inspired by visual attention mechanism [84] The latest works developed for this task are those of Kim et al [89] and Song et al [90]. The first is an innovative method based on a one-stage detector that aims to reduce the high computational costs required by large-scale datasets.…”

Section: ) Clothes Landmark Detectionmentioning

confidence: 99%

Deep Learning Approaches for Fashion Knowledge Extraction From Social Media: A Review

et al. 2022

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Fashion knowledge encourages people to properly dress and faces not only physiological necessity of users, but also the requirement of social practices and activities. It usually includes three jointly related aspects of: occasion, person and clothing. Nowadays, social media platforms allow users to interact with each other online to share opinions and information. The use of social media sites such as Instagram has already spread to almost every fashion brand and been evaluated as business take-off tools. With the heightened use of social media as a means of marketing communication for fashion brands, it has become necessary to empirically analyse and extract fashion knowledge from them. Thus, social brands are investing on them. In this way, they can understand the consumer's preferences. This change is also having a significant impact on social media data analysis. To solve this issue, the Deep learning (DL) methods are proven to be effective solutions due to their automatic learning capability. However, little systematic work currently exists on how researchers have applied DL for analysing fashion knowledge from social media data. Hence, this contribution outlines DL-based techniques for social media data related to fashion domain. In this study, a review of the dataset within the fashion world and the DL methods applied on, it is presented to help out new researchers interested in this subject. In particular, five different tasks will be considered: Object Detection, that includes Clothes Landmark Detection, Clothes Parsing and Product Retrieval, Fashion Classification, Clothes Generation, Automatic Fashion Knowledge Extraction and Clothes Recommendation. Therefore, the purpose of this paper is to underline the multiple applications within the fashion world using deep learning techniques. However, this review does not cover all the methods used: in fact, only Deep Learning methods have been analyzed. This choice was made since, given the huge amount of fashion social media data that has been collected, Deep Learning methods achieve the best performance both in terms of accuracy and time. Limitations point towards unexplored areas for future investigations, serving as useful guidelines for future research directions.

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Multiple-Clothing Detection and Fashion Landmark Estimation Using a Single-Stage Detector

Cited by 20 publications

References 33 publications

Occlusion Robust Wheat Ear Counting Algorithm Based on Deep Learning

Occlusion Robust Wheat Ear Counting Algorithm Based on Deep Learning

Underwater Object Detection Based on Improved EfficientDet

Deep Learning Approaches for Fashion Knowledge Extraction From Social Media: A Review

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